ggplot_quarto

Author

Lauren Patel

Importing Data

From excel

#dataframe <- read_excel("~/Research/path/to/folder/file.xlsx")
#View(dataframe)

From csv/tsv

Use read.table function: first argument specify path to file, sep argument tells R what separates the columns of your data (space, comma, or tab). Example below is for tab separated TSV. header = T (header = True) means the first row is the headers for columns.

RGFP_input_tagLen <- read.table("~/Research/LH_57_csRNA/Hela_sync_RGFP_input/tagLengthDistribution.txt", 
      sep="\t", header = T)

Hiding Code

You can embed plots without showing code:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

Data cleanup

Remove a pattern from a column name

Below example replaces “synchela” with “_”

#dataframe %>% rename_with(~ gsub("_synchela_", "_", .x), contains("_synchela"))

TidyR

`gather()` (outdated, use pivot_longer now!)

Convention: Gather(new independent var, new dependent var, column1, column2, column3, columnN)

puts column names in new independent var
values in new dependent var

LH51_hg38only_phredtrim_mapq_tidy <- LH51_phredtrim_hg38_MAPQscores %>% gather(Sample, Counts, a_count, b_count, c_count, d_count, q_count, r_count, s_count, t_count)

`pivot_longer()`

counts_megapeaks_hg38_LH56_tidy <- counts_megapeaks_hg38_LH56_clean %>% 
  select(PeakID, contains("Tag")) %>%    
  pivot_longer(
      cols = !PeakID,           # pivot all cols except PeakID
      names_to = "Sample",      # sample names become factor col
      values_to = "Peak_Tags")  # all values in new col

Base Plots

Line

Plot_line <- ggplot(data=dataframe, aes(x=xvariable)) + geom_line(aes(y=yvariable1, color= ”name/hexcode”)), + geom_line(aes(y=yvariable2, color= “name/hexcode”)), + geom_line(etc.) + labs (x = “X Axis Label”, y = “Y Axis Label”, title = “Plot title”)

ggplot(mtcars, aes(wt, mpg, group = cyl)) + 
  geom_line(aes(color = cyl)) + 
  labs(title = "Miles per Gallon vs Car Weight, Colored by # of Cylinders")

Histogram

density_hist <- ggplot(data=iris, aes(x=Sepal.Width, fill=Species))
density_hist + 
  geom_density(stat="density", alpha=I(0.2)) + 
  xlab("Sepal Width") + 
  ylab("Density") + 
  ggtitle("Histogram & Density Curve of Sepal Width")

Pie

input_presence <- data.frame(input_status = c("sufficient inputs", "some inputs missing", "no inputs present"), paper_count = c(27, 12, 14))

library(scales)


Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

input_piechart <- ggplot(input_presence, aes(x="", y= paper_count, fill=input_status)) +
  geom_bar(stat="identity", width=1, alpha = 0.9) +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(y = paper_count/3 + c(0, cumsum(paper_count)[-length(paper_count)]), 
            label = paper_count), size=5) + 
  scale_fill_manual(values = c("firebrick3", "darkorange2","goldenrod1"), name = "Status of inputs") + 
  labs(title = "Status of Non-IP Input Controls in ChIP-seq Spike-in Datasets",
       subtitle = "n = 53")

input_piechart

Scatterplot

ggplot(diamonds, aes(carat, price, color = clarity)) +
  geom_point()

Remove outline of point (for scatterplots with many overlapping points)

ggplot(diamonds, aes(carat, price, color = clarity)) +
  geom_point(stroke = NA, alpha = 0.3)

NOTE: for scatterplots the title in labs() must come first and be lower case

Density Scatterplots

Make some data

# Data
a <- data.frame(x=rnorm(20000, 10, 1.9), y=rnorm(20000, 10, 1.2) )
b <- data.frame(x=rnorm(20000, 14.5, 1.9), y=rnorm(20000, 14.5, 1.9) )
c <- data.frame(x=rnorm(20000, 9.5, 1.9), y=rnorm(20000, 15.5, 1.9) )
data <- rbind(a,b,c)
 
# Basic scatterplot
ggplot(data, aes(x=x, y=y) ) +
  geom_point()

In the above graph, where is the highest density of points?

Density plots - prevent overplotting Instead we should plot a 2D histogram

Default:

# 2d histogram with default option
ggplot(data, aes(x=x, y=y) ) +
  geom_bin2d() +
  theme_bw()

Make it pretty:

# Bin size control + color palette
ggplot(data, aes(x=x, y=y) ) +
  geom_bin2d(bins = 70) +
  scale_fill_continuous(type = "viridis") +
  theme_bw()

2D Density plot with Hex bins

ggplot(data, aes(x=x, y=y)) +
  geom_hex() +
  theme_bw()

Warning: Computation failed in `stat_binhex()`.
Caused by error in `compute_group()`:
! The package "hexbin" is required for `stat_bin_hex()`.

# Bin size control + color palette
ggplot(data, aes(x=x, y=y)) + 
  geom_hex(bins = 70) + 
  scale_fill_continuous(type = "viridis")

Warning: Computation failed in `stat_binhex()`.
Caused by error in `compute_group()`:
! The package "hexbin" is required for `stat_bin_hex()`.

2d distribution with geom_density_2d or stat_density_2d

Contour only

ggplot(data, aes(x=x, y=y) ) +
  geom_density_2d()

Area only

ggplot(data, aes(x=x, y=y) ) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon")

Warning: The dot-dot notation (`..level..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(level)` instead.

Area + Contour

ggplot(data, aes(x=x, y=y) ) +
  stat_density_2d(aes(fill = ..level..), 
                  geom = "polygon", colour="white")

Using Raster

ggplot(data, aes(x=x, y=y) ) +
  stat_density_2d(aes(fill = ..density..), geom = "raster", contour = FALSE) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(
    legend.position='none'
  )

Scatterplot + Smoothing Line

ggplot(diamonds, aes(carat, price, color = clarity)) +
  geom_point() +
  geom_smooth() + 
  labs(title = "Price of Diamonds by Cut and Clarity")

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Add Linear model

ggplot(diamonds, aes(carat, price, color = clarity)) +
  geom_point(alpha = 0.1) +
  geom_smooth(method = "lm") + 
  labs(title = "Price of Diamonds by Cut and Clarity")

`geom_smooth()` using formula = 'y ~ x'

Text Scatter Plot

# Base layer
plt_mpg_vs_wt <- ggplot(mtcars, aes(wt, mpg))

# Use text layer and map cyl to label
plt_mpg_vs_wt +
  geom_text(aes(label = mtcars$cyl)) + 
  labs(title = "MPG vs weight for Cars")

Warning: Use of `mtcars$cyl` is discouraged.
ℹ Use `cyl` instead.

Above plot is the weight of the car vs the miles per gallon, with each “point” being labeled with the number of cylinders the car has (a categorical variable, values either 4, 6 or 8)

Label points with text of row names:

ggplot(mtcars, aes(wt, mpg, color = cyl)) + 
  geom_text(label = rownames(mtcars), color = "red")

Barplots - Stacked

ggplot(iris, aes(Sepal.Length, fill = Species)) +
  geom_bar() + 
  labs(x = "Species", y = "Sepal Length")

Manual Color Scheme and Labeling

palette <- c(setosa = "#387EB8", versicolor = "#D11A1C", virginica = "#314EB9")
ggplot(iris, aes(Sepal.Length, fill = Species)) +
  geom_bar() +
  labs(x = "Species", y = "Count") +
  # Set the fill color scale
  scale_fill_manual("Transmission", values = palette)

Barplots - side by side

# Set the position
ggplot(iris, aes(Sepal.Length, fill = Species)) +
  geom_bar(position = "dodge") +
  labs(x = "Species", y = "Count") + 
  scale_fill_manual("Species", values = palette)

Violin Plot

ggplot(data = diamonds, aes(x=cut, y=price, fill = cut)) + 
  geom_violin()

Dotplot (2 category variables)

setwd("~/Research/R_for_fun/data_vis")
species <- read_csv("national_parks_biodiv/species.csv")
species_clean <- species %>% 
  filter(Occurrence == "Present") %>%
  rename_with(~ gsub(" ", "_", .x), contains(" "))
# removed spaces in variable names

species_clean$Park_Name <- gsub(" National Park", "", 
                                as.character(species_clean$Park_Name))
# removed "National Park" from Park_Name

acadia <- filter(species_clean, Park_Name == "Acadia")
ggplot(data = acadia) + 
  aes(x = Category, y = Abundance, color = Abundance) + 
  geom_count() + 
  labs(title = "Abundance of Biodiversity in \nAcadia National Park")
## linebreak in title

Ridgeline

library(ggridges)

In most cases you won’t need a legend here since each row of the ridgeline plot is clearly visible. Repress the legend with theme(legend.position = "none")

ggplot(diamonds) + 
  aes(x = price, y = clarity, fill = clarity) + 
  geom_density_ridges() + 
  theme(legend.position = "none")

Picking joint bandwidth of 403

Can also make a histogram:

ggplot(diamonds) + 
  aes(x = price, y = clarity, fill = clarity) + 
  geom_density_ridges(alpha = 0.6, stat = "binline", bins = 50) + 
  theme(legend.position = "none")

You can also map the color fill to a numeric variable (for instance the price on the x axis). There are two requirements:

Use geom_density_ridges_gradient() or geom_ridgeline_gradient()
Instead of direcly settging the fill to a variable (ex. fill = price in the example below) you have to set it to call the variable you have already defined. i.e. if you want fill = price, and x = price, then fill = stat(x).

Also note I used log(price) because otherwise the upper outliers in price warp the scale and no changes in color are noticeable (i.e. purely for illustrative purposes)

ggplot(diamonds) + 
  aes(x = log(price), y = clarity, fill = stat(x)) + 
  geom_density_ridges_gradient(scale = 2) + 
  theme(legend.position = "none") + 
  scale_fill_distiller("RdYlBu")

Warning: `stat(x)` was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(x)` instead.

Picking joint bandwidth of 0.144

Marginal Histograms

library(ggExtra)

diamonds_scatter <- ggplot(diamonds, aes(carat, price, color = clarity)) +
  geom_point() +
  labs(title = "Price of Diamonds by Cut and Clarity")

ggMarginal(diamonds_scatter, groupColour = TRUE, groupFill = TRUE)

Add-ons

Set Properties to Categorical Variable

In the geom, inside aes() set a property (i.e. alpha, shape, size, etc.) = Categorial Variable

Add scale_property_manual(values = c(value1, value2,etc), name = "Categorial Variable", labels = c("Category 1", "Category 2")) where “property” is alpha, shape, color, etc.

A big advantage of this method is you can set the name of the legend and the categories within the scale_property_manual, so if the variable name is long/cumbersome you can make the legend easier to read

mtcars$cyl = as.factor(mtcars$cyl)
ggplot(mtcars, aes(wt, mpg, group = cyl)) + 
  geom_point(aes(alpha = cyl), color = "firebrick", size = 4) + 
  labs(title = "Miles per Gallon vs Car Weight") +
  scale_alpha_manual(values = c(0.2, 0.5, 1), name = "Cylinders", labels = c("4", "6", "8"))

Faceting

Subplots based on a categorical variable

ggplot(diamonds, aes(carat, price, color = clarity)) +
  geom_point(alpha = 0.5, stroke = NA) +
  geom_smooth() + facet_wrap(~ clarity)

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Subplots in columns and rows (split by 2 variables)

# Base plot
insurance <- read_csv("~/Research/R_for_fun/archive/insurance.csv")
smoker_insurance <- ggplot(data = insurance) + 
  aes(x = smoker, y= charges) + 
  geom_jitter() 

# Graph by sex and region
smoker_insurance + 
  geom_jitter(aes(color = bmi)) + 
  facet_grid(cols = vars(region), rows = vars(sex))

Reordering subplots:

Reordering group factor levels

data_new<- rep(c("A","B", "C", "D"), each = 5)
col2 <- rep(c("12", "10"), each = 10)
data_new$Values <- col2

Warning in data_new$Values <- col2: Coercing LHS to a list

#View(data_new)
data_new$group <- factor(data_new$group,levels = c("B", "A", "C", "D"))
head(data_new)

[[1]]
[1] "A"

[[2]]
[1] "A"

[[3]]
[1] "A"

[[4]]
[1] "A"

[[5]]
[1] "A"

[[6]]
[1] "B"

faceting other plot types:

ggplot(diamonds) + 
  aes(x = log(price), y = clarity) + 
  geom_density_ridges_gradient(aes(x = log(price), fill = stat(x)), alpha = 0.4) + 
  scale_fill_distiller(palette = "Spectral") + 
  facet_wrap(~ color)

Picking joint bandwidth of 0.227

Picking joint bandwidth of 0.179

Picking joint bandwidth of 0.199

Picking joint bandwidth of 0.194

Picking joint bandwidth of 0.204

Picking joint bandwidth of 0.234

Picking joint bandwidth of 0.306

Faceting and zooming on subset of plot

Example of a plot that needs zooming:

public_data_submit_revisions <- read.delim("~/Research/spike_commentary/public_data_submit_revisions.tsv")

public_data_submit_revisions$filtered.spike_target <- as.numeric(public_data_submit_revisions$filtered.spike_target)

public_data_submit_revisions <- public_data_submit_revisions %>%
  group_by(Author) %>%
  drop_na(filtered.spike_target) %>%
  mutate(inputvar = (filtered.spike_target)/min(filtered.spike_target)) %>%
  mutate(meanvar = mean(inputvar))

public_data_submit_revisions <- public_data_submit_revisions %>%
  arrange(meanvar) %>%
  group_by(Author = factor(Author, levels = unique(Author))) %>%
  mutate(group = cur_group_id())

public_data_submit_revisions %>% 
  drop_na(inputvar) %>% group_by(Author) %>%
  arrange(meanvar) %>%
  ggplot() + 
  aes(x = as.factor(group), y = inputvar) + 
  geom_jitter(size = 4, alpha = 0.5, width = 0.2) + 
  geom_hline(yintercept = 1) + 
  labs(title = "Variation in input spike-in/target chromatin ratios", 
       y = "input/target", x = "Dataset") + 
  theme(legend.position = "none")

library(ggforce)
public_data_submit_revisions %>% 
  drop_na(filtered.spike_target) %>% ggplot() + 
  aes(x = as.factor(group), y = inputvar) + 
  geom_jitter(size = 6, alpha = 0.6, width = 0.1) + 
  geom_hline(yintercept = 1) + 
  labs(title = "Variation in input spike-in/target chromatin ratios", y = "Relative ratio of inputs within a dataset", 
       x = "Dataset") + 
  facet_zoom(ylim = c(0.7, 2.5))

Better way: break plot with facets into 1-19, then 20-23, and scale axes

can get break in rows, while scaling equally

scales = “free_x” removes the empty entries in each facet
space = “free” scales each facet by the range of x values, keeps them proportional

library(ggh4x)
library(RColorBrewer)

public_data_submit_revisions %>% 
  drop_na(inputvar) %>% ggplot() + 
  aes(x = as.factor(group), y = inputvar) + 
  geom_jitter(size = 3.5, alpha = 0.6, width = 0.17, color = "navy", pch=21, fill = "dodgerblue2", stroke = 1.5) + 
  geom_hline(yintercept = 1) + 
  labs(title = "Variation in input spike-in/target chromatin ratios", 
       y = "Relative ratio of inputs within a dataset",
       x = "Dataset") + 
  ggh4x::facet_grid2(~ group > 26, scales = "free", independent = "y", space = "free") +
   scale_color_manual(
      values = colorRampPalette(brewer.pal(9, "YlGnBu"))(13)[1:13])

Positioning

Scatterplot positioning

Default

ggplot(iris) + 
  aes(Sepal.Length, Sepal.Width, color = Species) +
  geom_point()

Jitter

ggplot(iris) +
  aes(Sepal.Length, Sepal.Width, color = Species) + 
  geom_jitter()

ggplot(iris) + 
  aes(Sepal.Length, Sepal.Width, color = Species) + 
  geom_point(position= "jitter")

Legend positioning

Use legend.position = c([HORIZONTAL], [VERTICAL]) where

[HORIZONTAL] = integer from (0, 1), 0 left, 1 right
[VERTICAL] = integer from (0, 1), 0 bottom, 1 top

ggplot(iris) + 
  aes(Sepal.Length, Sepal.Width, color = Species) +
  geom_point() + 
  coord_cartesian(ylim = c(2,5)) + 
  theme(legend.position = c(0.85, 0.83))

Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.

Add border to legend if inside graph to reduce chance of misinterpretation:

ggplot(iris) + 
  aes(Sepal.Length, Sepal.Width, color = Species) +
  geom_point() + 
  coord_cartesian(ylim = c(2,5)) + 
  theme(legend.position = c(0.85, 0.83), 
                            legend.background = element_rect(
                                    linewidth=0.7, linetype="solid", 
                                    colour ="grey20"))

Plot on log scale

Plot_type + scale_x_log10() + scale_y_log10()

ggplot(iris) + 
  aes(Sepal.Length, Sepal.Width, color = Species) + 
  geom_point(position= "jitter") + 
  scale_x_log10() + scale_y_log10()

Set x and y axes

Strategy 1: Scale_x_continuous

Remove points outside the viewing window (will affect regression lines, etc)

plot + scale_x_continuous(limits = c(xmin, xmax)) + scale_y_continuous(limits = c(ymin, ymax))

ggplot(iris) +
  aes(Sepal.Length, Sepal.Width, group = Species, color = Species) +
  geom_point(position= "jitter") + 
  scale_x_continuous(limits = c(4, 7)) + 
  scale_y_continuous(limits = c(1, 5))

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_point()`).

Strategy 2: coord_cartesian

Set viewing window without removing points that may fall outside (they just won’t be visible)

plot + coord_cartesian(xlim=c(xmin, xmax), ylim=c(ymin, ymax))

ggplot(iris) + 
  aes(Sepal.Length, Sepal.Width, group = Species, color = Species) + 
  geom_point(position= "jitter") + 
  coord_cartesian(xlim = c(4, 7), ylim = c(1, 5))

Add regression line

Plot_object + geom_smooth(method=lm)

For multiple regression lines by group, set group = Species within aes()

ggplot(iris) + 
  aes(Sepal.Length, Sepal.Width, group = Species, color = Species) + 
  geom_point(position= "jitter") + 
  geom_smooth(method = lm, fill = NA)

`geom_smooth()` using formula = 'y ~ x'

Add x = y line

Plot_object + geom_abline()

Manual tick lines

plot_object + scale_y_continuous(breaks=seq(ymin, ymax, interval))

Coloring

`Viridis` Colorblind-friendly library

Add with library(viridis)

library(viridis)

Loading required package: viridisLite


Attaching package: 'viridis'

The following object is masked from 'package:scales':

    viridis_pal

Continuous Color Schemes

Plotting for continuous variables

smoker_insurance + 
  geom_jitter(aes(color = bmi)) + 
  facet_grid(cols = vars(region), rows = vars(sex)) + 
  scale_color_viridis(option = "magma")

Discrete Color Schemes

knitr::include_graphics("C:\\Users\\lhodg\\Documents\\Research\\R_for_fun\\data_vis\\viridis_discrete.png")

Plotting for Discrete color schemes, make sure to set discrete = TRUE

ggplot(data = acadia) + 
  aes(x = Category, y = Abundance, color = Abundance) + 
  geom_count() + 
  labs(title = "Abundance of Biodiversity in Acadia National Park") +
  scale_color_viridis(option = "magma", discrete = TRUE)

R Color Brewer

Get hex codes from the color schemes:

brewer.pal(n=5,"Set1")

[1] "#E41A1C" "#377EB8" "#4DAF4A" "#984EA3" "#FF7F00"

Extend colors (if you need more colors, or want to exclude upper/lower colors)

colorRampPalette(brewer.pal(9, "YlGnBu"))(12)[4:11]

[1] "#B9E3B5" "#85CFBA" "#57BEC0" "#33A8C2" "#1D8CBD" "#2167AC" "#23479D"
[8] "#1D2D83"

ggplot(data = acadia) + 
  aes(x = Category, y = Abundance, color = Abundance) + 
  geom_count() + 
  labs(title = "Abundance of Biodiversity in Acadia National Park") +
  scale_color_manual(
      values = colorRampPalette(brewer.pal(9, "YlGnBu"))(10)[3:9],
      name = "Abundance")

non Rcolorbrewer palettes

library(grDevices) # library for color palettes
library(unikn) # library needed for plot below

Welcome to unikn (v1.0.0)!

simcol() searches for similar colors.

n <- 10

h1 <- hcl.colors(n, palette = "Dynamic")
h2 <- hcl.colors(n, palette = "Earth")
h3 <- hcl.colors(n, palette = "Terrain")
h4 <- hcl.colors(n, palette = "Berlin")
h5 <- hcl.colors(n, palette = "Oslo")
h6 <- hcl.colors(n, palette = "Lajolla")
h7 <- hcl.colors(n, palette = "Cork")
h8 <- hcl.colors(n, palette = "Vik")
h9 <- hcl.colors(n, palette = "Fall")
h10 <- hcl.colors(n, palette = "Sunset")
h11 <- hcl.colors(n, palette = "Purple-Orange")
h12 <- hcl.colors(n, palette = "Dark-Mint")
h13 <- hcl.colors(n, palette = "Peach")
h15 <- hcl.colors(n, palette = "Roma")

seecol(list(h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11, h12, h13, h15), 
       col_brd = "white", lwd_brd = 4, 
       title = "hcl.colors NOT in RColorBrewer (n = 10)", 
       pal_names = c("Dynamic", "Earth", "Terrain", "Berlin", "Oslo", "Lajolla", "Cork", "Vik", "Fall", "Sunset", "Purple-Orange", "Dark-Mint", "Peach", "Roma"))

See all hcl palettes:

hcl.pals()

  [1] "Pastel 1"      "Dark 2"        "Dark 3"        "Set 2"        
  [5] "Set 3"         "Warm"          "Cold"          "Harmonic"     
  [9] "Dynamic"       "Grays"         "Light Grays"   "Blues 2"      
 [13] "Blues 3"       "Purples 2"     "Purples 3"     "Reds 2"       
 [17] "Reds 3"        "Greens 2"      "Greens 3"      "Oslo"         
 [21] "Purple-Blue"   "Red-Purple"    "Red-Blue"      "Purple-Orange"
 [25] "Purple-Yellow" "Blue-Yellow"   "Green-Yellow"  "Red-Yellow"   
 [29] "Heat"          "Heat 2"        "Terrain"       "Terrain 2"    
 [33] "Viridis"       "Plasma"        "Inferno"       "Rocket"       
 [37] "Mako"          "Dark Mint"     "Mint"          "BluGrn"       
 [41] "Teal"          "TealGrn"       "Emrld"         "BluYl"        
 [45] "ag_GrnYl"      "Peach"         "PinkYl"        "Burg"         
 [49] "BurgYl"        "RedOr"         "OrYel"         "Purp"         
 [53] "PurpOr"        "Sunset"        "Magenta"       "SunsetDark"   
 [57] "ag_Sunset"     "BrwnYl"        "YlOrRd"        "YlOrBr"       
 [61] "OrRd"          "Oranges"       "YlGn"          "YlGnBu"       
 [65] "Reds"          "RdPu"          "PuRd"          "Purples"      
 [69] "PuBuGn"        "PuBu"          "Greens"        "BuGn"         
 [73] "GnBu"          "BuPu"          "Blues"         "Lajolla"      
 [77] "Turku"         "Hawaii"        "Batlow"        "Blue-Red"     
 [81] "Blue-Red 2"    "Blue-Red 3"    "Red-Green"     "Purple-Green" 
 [85] "Purple-Brown"  "Green-Brown"   "Blue-Yellow 2" "Blue-Yellow 3"
 [89] "Green-Orange"  "Cyan-Magenta"  "Tropic"        "Broc"         
 [93] "Cork"          "Vik"           "Berlin"        "Lisbon"       
 [97] "Tofino"        "ArmyRose"      "Earth"         "Fall"         
[101] "Geyser"        "TealRose"      "Temps"         "PuOr"         
[105] "RdBu"          "RdGy"          "PiYG"          "PRGn"         
[109] "BrBG"          "RdYlBu"        "RdYlGn"        "Spectral"     
[113] "Zissou 1"      "Cividis"       "Roma"

hcl.swatch <- function(type = NULL, n = 5, nrow = 11,
  border = if (n < 15) "black" else NA) {
    palette <- hcl.pals(type)
    cols <- sapply(palette, hcl.colors, n = n)
    ncol <- ncol(cols)
    nswatch <- min(ncol, nrow)

    par(mar = rep(0.1, 4),
        mfrow = c(1, min(5, ceiling(ncol/nrow))),
        pin = c(1, 0.5 * nswatch),
        cex = 0.7)

    while (length(palette)) {
        subset <- 1:min(nrow, ncol(cols))
        plot.new()
        plot.window(c(0, n), c(0, nrow + 1))
        text(0, rev(subset) + 0.1, palette[subset], adj = c(0, 0))
        y <- rep(subset, each = n)
        rect(rep(0:(n-1), n), rev(y), rep(1:n, n), rev(y) - 0.5,
             col = cols[, subset], border = border)
        palette <- palette[-subset]
        cols <- cols[, -subset, drop = FALSE]
    }

    par(mfrow = c(1, 1), mar = c(5.1, 4.1, 4.1, 2.1), cex = 1)
}
hcl.swatch("qualitative")

hcl.swatch("sequential")

hcl.swatch("diverging")

hcl.swatch("divergingx")

Manual Color Scheme and Labeling

# Define your palette of choice
iris_palette <- c(setosa = "slateblue", versicolor = "seagreen", virginica = "orchid")

# Plot using your palette, the first argument in scale_fill_manual is the legend title
ggplot(iris, aes(Petal.Width, fill = Species)) +
  geom_bar() +
  labs(x = "Species", y = "Count") +
  scale_fill_manual("Species", values = iris_palette)

Labels

All Font Sizes Any ## indicates numerical input, XX indicates text input

Plot_object + theme(axis.text.x = element_text(size=##), axis.text.y = element_text(size=##), axis.title = element_text(size=##), etc.)

Options for text size changing:

axis.text.x
axis.text.y
strip.text.x : text in facet labels

custom legend labels

For line/dot plots: scale_color_discrete or scale_color_manual

scale_color_discrete(name = "Legend Title", labels = c("label1", "label2", "label3", "75%","95%", "100%"))

Example

ggplot(iris) +
  aes(Sepal.Length, Sepal.Width, group = Species, color = Species) +
  geom_point() + 
  scale_color_manual(
    name = "Species", 
    labels = c("Setosa", "Versicolor", "Virginica"), 
    values = c("slateblue", "seagreen", "orchid")
  )

For bar/violin/similar: scale_fill_discrete

rotate labels

Plot_object + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Format Date Labels

Standard date format: yyyy-mm-dd

Make a time series dataset:

set.seed(1234)
last_month <- Sys.Date() - 0:29
df <- data.frame(
  date = last_month,
  price = runif(30)
)

Plot:

ggplot(data=df, aes(x = date, y = price)) +
  geom_line()

Format dates:

Weekday name: use %a and %A for abbreviated and full weekday name, respectively
Month name: use %b and %B for abbreviated and full month name, respectively
%d: day of the month as decimal number
%U: week of the year as decimal number (00-53)
%Y: Year with century

ggplot(data=df, aes(x = date, y = price)) +
  geom_line() + 
  scale_x_date(date_labels = "%b/%d")

ggplot(data=df, aes(x = date, y = price)) +
  geom_line() + scale_x_date(date_labels = "%U")

format axes numbers

sci notation -> full numbers

options(scipen = 999)
library(gapminder)
ggplot(data = gapminder %>% filter(year == 2007) %>% arrange(-pop) %>% top_n(10, wt = pop)) + 
  aes(x = country, y = pop) + 
  geom_bar(stat = "identity")

full numbers -> Add commas

scale_y_continuous(labels = scales::comma)

ggplot(data = gapminder %>% filter(year == 2007) %>% arrange(-pop) %>% top_n(10, wt = pop)) + 
  aes(x = country, y = pop) + 
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::comma)

Full numbers -> Millions or Billions

labels = scales::label_number_si()

ggplot(data = gapminder %>% filter(year == 2007) %>% arrange(-pop) %>% top_n(10, wt = pop)) + 
  aes(x = country, y = pop) + 
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::label_number(scale_cut == cut_number()))

Linebreak in plot labels

title = "text here \ntext here"

acadia <- filter(species_clean, Park_Name == "Acadia")
ggplot(data = acadia) + 
  aes(x = Category, y = Abundance, color = Abundance) + 
  geom_count() + 
  labs(title = "Abundance of Biodiversity in \nAcadia National Park")

Combining plots

GridExtra - Gridarrange

library(gridExtra)


Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine

Some dummy plots

p1 <- ggplot(data=iris) + 
  aes(x=Sepal.Width, fill=Species) + 
  geom_density(stat="density", alpha=I(0.2)) + 
  xlab("Sepal Width") + ylab("Density") + 
  ggtitle("Histogram & Density Curve of Sepal Width")
  
p2 <- ggplot(data, aes(x=x, y=y) ) +
  geom_bin2d(bins = 70) +
  scale_fill_continuous(type = "viridis") +
  theme_bw()
  
p3 <- plt_mpg_vs_wt +
  geom_text(aes(label = mtcars$cyl)) + 
  labs(title = "MPG vs weight for Cars")

p4 <- ggplot(data = diamonds) + 
  aes(x=cut, y=price, fill = cut) +
  geom_violin() + 
  theme(legend.position = "none")
  

p5 <- ggplot(iris) +  
  aes(Sepal.Length, Sepal.Width, color = Species) + 
  geom_jitter()

Combine with grid.arrange

# make a matrix of the layout - NA's are empty placeholders, each vector makes a row
lay <- rbind(c(1, NA, NA),
             c(2, 3, NA), 
             c(4, 5, NA))

# gridarrange takes the plots (in order of the layout matrix) then the layout matrix
# can also set custom widths/heights for each row if you want
grid.arrange(p1, p2, p3, p4, p5,
  layout_matrix = lay, 
  widths = c(5,6,5))

Importing Data

From excel

From csv/tsv

Hiding Code

Data cleanup

Remove a pattern from a column name

TidyR

gather() (outdated, use pivot_longer now!)

pivot_longer()

Base Plots

Line

Histogram

Pie

Scatterplot

Density Scatterplots

2D Density plot with Hex bins

2d distribution with geom_density_2d or stat_density_2d

Scatterplot + Smoothing Line

Add Linear model

Text Scatter Plot

Barplots - Stacked

Manual Color Scheme and Labeling

Barplots - side by side

Violin Plot

Dotplot (2 category variables)

Ridgeline

Marginal Histograms

Add-ons

Set Properties to Categorical Variable

Faceting

Subplots based on a categorical variable

Subplots in columns and rows (split by 2 variables)

Reordering subplots:

faceting other plot types:

Faceting and zooming on subset of plot

Positioning

Scatterplot positioning

Legend positioning

Plot on log scale

Set x and y axes

Strategy 1: Scale_x_continuous

Strategy 2: coord_cartesian

Add regression line

Add x = y line

Manual tick lines

Coloring

Viridis Colorblind-friendly library

Continuous Color Schemes

Discrete Color Schemes

R Color Brewer

non Rcolorbrewer palettes

Manual Color Scheme and Labeling

Labels

custom legend labels

rotate labels

Format Date Labels

format axes numbers

sci notation -> full numbers

full numbers -> Add commas

Full numbers -> Millions or Billions

Linebreak in plot labels

Combining plots

GridExtra - Gridarrange

`gather()` (outdated, use pivot_longer now!)

`pivot_longer()`

`Viridis` Colorblind-friendly library